TMCS-ENN: A Temporal Feature-Correlation Cuckoo Search-Elman Neural Network for Sugar Raw Materials Demands Prediction

Abstract

The prediction of the demand for raw materials is of vital importance to modern industries. Most studies are based on traditional regression, linear programming, and other methods. Previous studies have often overlooked the characteristics of the sugar raw materials business and the influence of time factors on raw material demand, resulting in limited prediction accuracy. How to accurately predict the demand for sugar raw materials is one of the key issues for intelligent management. In view of the above problems, combined with the characteristics of the supply and demand cycle of sugar raw materials, this paper aims to predict the demand for raw materials based on their supply and demand in a real sugar company by optimizing the Elman neural network through the modified cuckoo search (MCS) algorithm with temporal features. This study proposes a temporal feature-correlation cuckoo search–Elman neural network (TMCS-ENN) for predicting the demand for sugar raw materials. The experimental results show that the accuracy of the TMCS-ENN model reaches 93.89%, a better performance than that achieved by existing models. Therefore, the study model effectively improves the accuracy of the demand forecast of sugar raw materials for companies. This output will be helpful for improving the production efficiency and automation level, as well as reducing costs.